Semantic Network Model for Sign Language Comprehension

TL;DR

This paper introduces a semantic network model for sign language comprehension that uses spreading activation to improve understanding of classifier predicates, offering a computational approach aligned with human cognitive processes.

Contribution

The paper presents a novel semantic network model with a spreading activation algorithm specifically designed for sign language comprehension, enhancing existing knowledge representation methods.

Findings

Improved performance in sign language comprehension tasks

Effective modeling of semantic relations in sign language

Demonstrated benefits of spreading activation in SNM

Abstract

In this study, the authors propose a computational cognitive model for sign language (SL) perception and comprehension with detailed algorithmic descriptions based on cognitive functionalities in human language processing. The semantic network model (SNM) that represents semantic relations between concepts, it is used as a form of knowledge representation. The proposed model is applied in the comprehension of sign language for classifier predicates. The spreading activation search method is initiated by labeling a set of source nodes (e.g. concepts in the semantic network) with weights or "activation" and then iteratively propagating or "spreading" that activation out to other nodes linked to the source nodes. The results demonstrate that the proposed search method improves the performance of sign language comprehension in the SNM.